Adaptive neural fault-tolerant control for a class of strict-feedback nonlinear systems with actuator and sensor faults

This paper investigates the adaptive neural fault-tolerant control problem for a class of strict-feedback nonlinear systems with simultaneous actuator and sensor faults. The faults considered in this paper are bias (lock-in-place), drift, loss of accuracy, and loss of effectiveness faults. Only one parameter law is updated at each step to reduce the computational burden. By utilizing the adaptive neural network backstepping control strategy, the closed-loop nonlinear system is guaranteed to be semi-globally uniform ultimate bounded, and all the signals are bounded. Finally, a simulation example is given to show the effectiveness of the proposed control strategy.